Predictors of Academic Performance in High School Students: The Longitudinal ASAP Study
Bibliographic record
Abstract
International Journal of Exercise Science 15(4): 616-631, 2022. Academic performance is influenced by multitude factors. However, little is known about their relative importance and how they evolve over time. The purpose of the present study was to determine the relative importance of cognitive control, physical, psychological and sociological factors as well as lifestyle habits in predicting academic performance in high school students using cross sectional and longitudinal approaches. One hundred and eighty-five grade seventh to ninth students (mean age: 13.1 ± 1.0 years old) from a single high school completed a 3-year prospective study. Academic performance, cognitive control, physical, psychological and sociological factors as well as lifestyle habits were assessed every year during the 3-year study. Results showed that different combinations of factors were found to predict academic performance measures in both male and female students at baseline and after a 3-year period. For example, in female students, screen time and VO2 max were found to be important predictors of academic performance, whereas working memory was the only recurring factor in predicting academic performance in male students. Moreover, our models were able to explain between 6.1 to 52.2% of the variation in the change of the different measures of academic performance. Results of the present study show that academic performance may be predicted by a wide range of multiple factors in high school students. Indeed, the factors that predicted academic performance varied between school subjects, sex and study design, highlighting the complexity of predicting academic performance in high school students.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".